


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
						from datasets import load_dataset







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       Optional[int] 			=		'microsoft/speecht5_tts'
			_UpperCamelCase		:       Optional[int] 			=		(
			    'This is a tool that reads an English text out loud. It takes an input named `text` which should contain the '
			    'text to read (in English) and returns a waveform object containing the sound.'
			)
			_UpperCamelCase		:       str 			=		'text_reader'
			_UpperCamelCase		:       List[str] 			=		SpeechTaProcessor
			_UpperCamelCase		:       Dict 			=		SpeechTaForTextToSpeech
			_UpperCamelCase		:       Optional[Any] 			=		SpeechTaHifiGan

			_UpperCamelCase		:       Dict 			=		['text']
			_UpperCamelCase		:       int 			=		['audio']




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						if self.post_processor is None:
									UpperCamelCase__       =							"microsoft/speecht5_hifigan"
						super().setup()




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case=None  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.pre_processor(text=snake_case   ,    return_tensors="pt"   ,    truncation=snake_case  )

						if speaker_embeddings is None:
									if not is_datasets_available():
												raise ImportError("Datasets needs to be installed if not passing speaker embeddings."  )

									UpperCamelCase__       =							load_dataset("Matthijs/cmu-arctic-xvectors"   ,    split="validation"  )
									UpperCamelCase__       =							torch.tensor(embeddings_dataset[7305]["xvector"]  ).unsqueeze(0  )

						return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						with torch.no_grad():
									return self.model.generate_speech(**snake_case  )







			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						with torch.no_grad():
									return self.post_processor(snake_case  ).cpu().detach()






def 							UpperCamelCase_(					_A :Union[str, Any]	)->		List[str]:
			UpperCamelCase__       =							[0] * len(_A	)
			UpperCamelCase__       =							[]
			UpperCamelCase__       =							[]
			UpperCamelCase__       =							0

			for values in graph.values():
						for i in values:
									indegree[i] += 1

			for i in range(len(_A	)	):
						if indegree[i] == 0:
									queue.append(_A	)

			while queue:
						UpperCamelCase__       =							queue.pop(0	)
						cnt += 1
						topo.append(_A	)
						for x in graph[vertex]:
									indegree[x] -= 1
									if indegree[x] == 0:
												queue.append(_A	)

			if cnt != len(_A	):
						print("Cycle exists"	)
			else:
						print(_A	)


# Adjacency List of Graph
__UpperCamelCase     		=							{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}







topological_sort(graph)


